Package {reaborn}

Title:	Statistical Data Visualization, the 'seaborn' Way
Version:	1.0.0
Description:	An 'R' port of the 'Python' 'seaborn' library. 'reaborn' mirrors the 'seaborn' public function API (identical function names, argument names, and defaults) and renders visually indistinguishable plots using 'ggplot2'. Because every 'reaborn' plot is a 'ggplot' object, it can be extended with the full 'ggplot2' grammar of graphics.
License:	BSD_3_clause + file LICENSE
Encoding:	UTF-8
Language:	en-US
RoxygenNote:	8.0.0
Depends:	R (≥ 4.1)
Imports:	ggplot2 (≥ 4.0.0), grDevices, grid, rlang, scales, stats
Suggests:	colorspace, ggbeeswarm, ggdendro, jsonlite, knitr, MASS, patchwork, ragg, rmarkdown, testthat (≥ 3.0.0), vdiffr
Config/testthat/edition:	3
VignetteBuilder:	knitr
URL:	https://reaborn.org, https://github.com/shawntz/reaborn
BugReports:	https://github.com/shawntz/reaborn/issues
NeedsCompilation:	no
Packaged:	2026-06-25 19:16:10 UTC; shawnschwartz
Author:	Shawn Schwartz [aut, cre]
Maintainer:	Shawn Schwartz <shawn.t.schwartz@gmail.com>
Repository:	CRAN
Date/Publication:	2026-06-30 20:40:02 UTC

reaborn: Statistical Data Visualization, the Seaborn Way

Description

reaborn is an R port of the Python seaborn library. It mirrors seaborn's public function API (identical names, arguments, and defaults) and produces visually indistinguishable plots built on ggplot2. Every reaborn plot is a ggplot object, so it composes with the full ggplot2 grammar of graphics.

Author(s)

Maintainer: Shawn Schwartz shawn.t.schwartz@gmail.com (ORCID)

Authors:

• Shawn Schwartz shawn.t.schwartz@gmail.com (ORCID)

See Also

Useful links:

• https://reaborn.org

• https://github.com/shawntz/reaborn

• Report bugs at https://github.com/shawntz/reaborn/issues

Add to a reaborn plot, preserving its class

Description

Ensures ⁠reaborn_plot + <ggplot component>⁠ stays a reaborn_plot so the composed object keeps printing through reaborn and remains chainable.

Usage

## S3 method for class 'reaborn_plot'
e1 + e2

Arguments

Value

A reaborn plot.

A faceted grid of plots

Description

Lightweight port of seaborn.FacetGrid. In reaborn, faceting is usually done by adding + ggplot2::facet_wrap()/facet_grid() to a plot, or via the figure-level functions (relplot, displot, catplot, lmplot). This constructor returns a base plot you can map geoms onto.

Usage

FacetGrid(
  data,
  row = NULL,
  col = NULL,
  hue = NULL,
  col_wrap = NULL,
  height = 3,
  aspect = 1,
  palette = NULL
)

Arguments

Value

A reaborn_plot.

Get the parameters that control the general style of the plots

Description

Port of seaborn.axes_style. Returns the resolved style definition.

Usage

axes_style(style = NULL, rc = NULL)

set_style(style = NULL, rc = NULL)

Arguments

Value

A named list describing the style.

Show point estimates and errors as bars

Description

Port of seaborn.barplot. Bar heights are an aggregate (default mean) with a bootstrap CI error bar. Returns a reaborn_plot.

Usage

barplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  order = NULL,
  hue_order = NULL,
  estimator = "mean",
  errorbar = list("ci", 95),
  n_boot = 1000,
  seed = NULL,
  units = NULL,
  weights = NULL,
  orient = NULL,
  color = NULL,
  palette = NULL,
  saturation = 0.75,
  fill = TRUE,
  width = 0.8,
  dodge = "auto",
  gap = 0,
  capsize = 0,
  err_kws = NULL,
  legend = "auto",
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Draw an enhanced box plot for larger datasets

Description

Port of seaborn.boxenplot (letter-value plot). Returns a reaborn_plot.

Usage

boxenplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  order = NULL,
  hue_order = NULL,
  orient = NULL,
  color = NULL,
  palette = NULL,
  saturation = 0.75,
  fill = TRUE,
  width = 0.8,
  gap = 0,
  linewidth = NULL,
  linecolor = NULL,
  width_method = "exponential",
  k_depth = "tukey",
  outlier_prop = 0.007,
  trust_alpha = 0.05,
  showfliers = TRUE,
  legend = "auto",
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Draw a box plot

Description

Port of seaborn.boxplot. Returns a reaborn_plot.

Usage

boxplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  order = NULL,
  hue_order = NULL,
  orient = NULL,
  color = NULL,
  palette = NULL,
  saturation = 0.75,
  fill = TRUE,
  dodge = "auto",
  width = 0.8,
  gap = 0,
  whis = 1.5,
  linecolor = "auto",
  linewidth = NULL,
  fliersize = NULL,
  legend = "auto",
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Figure-level interface for categorical plots

Description

Port of seaborn.catplot. Dispatches to stripplot (kind = "strip"), boxplot, barplot, pointplot, or countplot and adds row/col faceting. Returns a faceted reaborn_plot.

Usage

catplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  row = NULL,
  col = NULL,
  kind = "strip",
  estimator = "mean",
  errorbar = list("ci", 95),
  n_boot = 1000,
  seed = NULL,
  units = NULL,
  weights = NULL,
  order = NULL,
  hue_order = NULL,
  row_order = NULL,
  col_order = NULL,
  col_wrap = NULL,
  height = 5,
  aspect = 1,
  orient = NULL,
  color = NULL,
  palette = NULL,
  legend = "auto",
  facet_kws = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Plot a hierarchically-clustered heatmap

Description

Port of seaborn.clustermap. Reorders rows/columns by hierarchical clustering and draws dendrograms alongside the heatmap. Returns a patchwork composition.

Usage

clustermap(
  data,
  method = "average",
  metric = "euclidean",
  z_score = NULL,
  standard_scale = NULL,
  row_cluster = TRUE,
  col_cluster = TRUE,
  cmap = NULL,
  dendrogram_ratio = 0.2,
  ...
)

Arguments

Value

A reaborn_plot (patchwork).

Return a list of colors or a continuous colormap defining a palette

Description

Port of seaborn.color_palette. Possible palette values include the name of a seaborn palette (deep, muted, bright, pastel, dark, colorblind), a matplotlib colormap name, "husl"/"hls", a cubehelix shorthand ("ch:..."), "light:<color>", "dark:<color>", "blend:<c1>,<c2>", or a sequence of colors.

Usage

color_palette(palette = NULL, n_colors = NULL, desat = NULL, as_cmap = FALSE)

hls_palette(n_colors = 6, h = 0.01, l = 0.6, s = 0.65, as_cmap = FALSE)

husl_palette(n_colors = 6, h = 0.01, s = 0.9, l = 0.65, as_cmap = FALSE)

dark_palette(
  color,
  n_colors = 6,
  reverse = FALSE,
  as_cmap = FALSE,
  input = "rgb"
)

light_palette(
  color,
  n_colors = 6,
  reverse = FALSE,
  as_cmap = FALSE,
  input = "rgb"
)

diverging_palette(
  h_neg,
  h_pos,
  s = 75,
  l = 50,
  sep = 1,
  n = 6,
  center = "light",
  as_cmap = FALSE
)

blend_palette(colors, n_colors = 6, as_cmap = FALSE, input = "rgb")

mpl_palette(name, n_colors = 6, as_cmap = FALSE)

cubehelix_palette(
  n_colors = 6,
  start = 0,
  rot = 0.4,
  gamma = 1,
  hue = 0.8,
  light = 0.85,
  dark = 0.15,
  reverse = FALSE,
  as_cmap = FALSE
)

Arguments

Value

A character vector of hex colors, or a reaborn_cmap.

Show value counts as bars

Description

Port of seaborn.countplot. Returns a reaborn_plot.

Usage

countplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  order = NULL,
  hue_order = NULL,
  orient = NULL,
  color = NULL,
  palette = NULL,
  saturation = 0.75,
  fill = TRUE,
  stat = "count",
  width = 0.8,
  dodge = "auto",
  gap = 0,
  legend = "auto",
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Decrease the saturation of a color

Description

Port of seaborn.desaturate.

Usage

desaturate(color, prop)

Arguments

Value

A hex color string.

Remove spines from a plot

Description

Port of seaborn.despine. Returns a ggplot2 theme partial that removes the requested plot borders and (for borderless styles) draws explicit axis lines on the kept sides. Add it to a reaborn/ggplot plot: p + despine().

Usage

despine(
  fig = NULL,
  ax = NULL,
  top = TRUE,
  right = TRUE,
  left = FALSE,
  bottom = FALSE,
  offset = NULL,
  trim = FALSE
)

Arguments

Value

A ggplot2 theme object to add to a plot.

Figure-level interface for distribution plots

Description

Port of seaborn.displot. Draws histplot (kind = "hist"), kdeplot (kind = "kde"), or ecdfplot (kind = "ecdf") onto a grid of facets. Returns a faceted reaborn_plot.

Usage

displot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  row = NULL,
  col = NULL,
  weights = NULL,
  kind = "hist",
  rug = FALSE,
  rug_kws = NULL,
  palette = NULL,
  hue_order = NULL,
  hue_norm = NULL,
  color = NULL,
  col_wrap = NULL,
  row_order = NULL,
  col_order = NULL,
  legend = TRUE,
  height = 5,
  aspect = 1,
  facet_kws = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Who's a good boy?

Description

Port of seaborn.dogplot (an easter egg). Prints an affirmation.

Usage

dogplot(...)

Arguments

Value

Invisibly NULL.

Plot an empirical cumulative distribution function

Description

Port of seaborn.ecdfplot. Returns a reaborn_plot.

Usage

ecdfplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  weights = NULL,
  stat = "proportion",
  complementary = FALSE,
  palette = NULL,
  hue_order = NULL,
  hue_norm = NULL,
  legend = TRUE,
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Plot rectangular data as a color-encoded matrix

Description

Port of seaborn.heatmap. Returns a reaborn_plot.

Usage

heatmap(
  data,
  vmin = NULL,
  vmax = NULL,
  cmap = NULL,
  center = NULL,
  robust = FALSE,
  annot = NULL,
  fmt = ".2g",
  annot_kws = NULL,
  linewidths = 0,
  linecolor = "white",
  cbar = TRUE,
  square = FALSE,
  xticklabels = "auto",
  yticklabels = "auto",
  mask = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Plot a univariate or bivariate histogram

Description

Port of seaborn.histplot. Returns a reaborn_plot.

Usage

histplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  weights = NULL,
  stat = "count",
  bins = "auto",
  binwidth = NULL,
  binrange = NULL,
  discrete = NULL,
  cumulative = FALSE,
  common_bins = TRUE,
  common_norm = TRUE,
  multiple = "layer",
  element = "bars",
  fill = TRUE,
  shrink = 1,
  kde = FALSE,
  kde_kws = NULL,
  palette = NULL,
  hue_order = NULL,
  hue_norm = NULL,
  color = NULL,
  legend = TRUE,
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Draw a bivariate plot with marginal distributions

Description

Port of seaborn.jointplot. Returns a patchwork composition (printable and saveable like any reaborn plot).

Usage

jointplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  kind = "scatter",
  height = 6,
  ratio = 5,
  space = 0.2,
  color = NULL,
  palette = NULL,
  ...
)

Arguments

Value

A reaborn_plot (patchwork).

Plot a univariate or bivariate kernel density estimate

Description

Port of seaborn.kdeplot. The KDE matches scipy.stats.gaussian_kde exactly. Returns a reaborn_plot.

Usage

kdeplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  weights = NULL,
  palette = NULL,
  hue_order = NULL,
  hue_norm = NULL,
  color = NULL,
  fill = NULL,
  multiple = "layer",
  common_norm = TRUE,
  common_grid = FALSE,
  cumulative = FALSE,
  bw_method = "scott",
  bw_adjust = 1,
  log_scale = NULL,
  levels = 10,
  thresh = 0.05,
  gridsize = 200,
  cut = 3,
  clip = NULL,
  legend = TRUE,
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Draw a line plot with aggregation and error bands

Description

Port of seaborn.lineplot. When the data has repeated observations per x value, they are aggregated (default: mean) and an error band (default: 95% bootstrap CI) is drawn. Returns a reaborn_plot.

Usage

lineplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  size = NULL,
  style = NULL,
  units = NULL,
  weights = NULL,
  palette = NULL,
  hue_order = NULL,
  hue_norm = NULL,
  sizes = NULL,
  size_order = NULL,
  size_norm = NULL,
  dashes = TRUE,
  markers = NULL,
  style_order = NULL,
  estimator = "mean",
  errorbar = list("ci", 95),
  n_boot = 1000,
  seed = NULL,
  orient = "x",
  sort = TRUE,
  err_style = "band",
  err_kws = NULL,
  legend = "auto",
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Figure-level interface for regression plots

Description

Port of seaborn.lmplot. Draws regplot across a grid of facets and/or hue groups. Returns a reaborn_plot.

Usage

lmplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  col = NULL,
  row = NULL,
  palette = NULL,
  col_wrap = NULL,
  height = 5,
  aspect = 1,
  order = 1,
  logistic = FALSE,
  lowess = FALSE,
  robust = FALSE,
  logx = FALSE,
  ci = 95,
  n_boot = 1000,
  seed = NULL,
  scatter = TRUE,
  fit_reg = TRUE,
  hue_order = NULL,
  row_order = NULL,
  col_order = NULL,
  legend = TRUE,
  facet_kws = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Load an example dataset from the seaborn-data repository

Description

Port of seaborn.load_dataset. Bundled datasets (penguins, tips, iris, flights) load offline; others download from the seaborn-data repo and cache.

Usage

load_dataset(name, cache = TRUE, data_home = NULL, ...)

get_dataset_names()

Arguments

Value

A data.frame, with categorical columns coerced to ordered factors matching seaborn.

For get_dataset_names, a character vector of available dataset names.

Reposition a plot's legend

Description

Port of seaborn.move_legend. Returns a theme partial controlling legend position. Add it to a plot: p + move_legend("upper right").

Usage

move_legend(obj = NULL, loc = "best", ...)

Arguments

Value

A ggplot2 theme object to add to a plot.

Plot pairwise relationships in a dataset

Description

Port of seaborn.pairplot. Returns a patchwork matrix of scatter plots with univariate distributions on the diagonal.

Usage

pairplot(
  data,
  vars = NULL,
  hue = NULL,
  kind = "scatter",
  diag_kind = "auto",
  palette = NULL,
  height = 2.5,
  aspect = 1,
  corner = FALSE,
  ...
)

Arguments

Value

A reaborn_plot (patchwork).

Plot the values in a color palette as a horizontal array

Description

Port of seaborn.palplot. Returns a reaborn_plot.

Usage

palplot(pal, size = 1)

Arguments

Value

A reaborn_plot.

Get the parameters that control the scaling of plot elements

Description

Port of seaborn.plotting_context. Returns a named list of resolved sizes.

Usage

plotting_context(context = NULL, font_scale = 1, rc = NULL)

set_context(context = NULL, font_scale = 1, rc = NULL)

Arguments

Value

A named list of context parameters.

Show point estimates and errors with markers

Description

Port of seaborn.pointplot. Returns a reaborn_plot.

Usage

pointplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  order = NULL,
  hue_order = NULL,
  estimator = "mean",
  errorbar = list("ci", 95),
  n_boot = 1000,
  seed = NULL,
  units = NULL,
  weights = NULL,
  color = NULL,
  palette = NULL,
  markers = "o",
  linestyles = "-",
  dodge = FALSE,
  orient = NULL,
  capsize = 0,
  legend = "auto",
  err_kws = NULL,
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Python literal compatibility values

Description

True, False, and None are provided so that seaborn Python code pasted into R (e.g. histplot(data = df, x = "a", kde = True)) runs unchanged. They are exactly TRUE, FALSE, and NULL.

Usage

True

False

None

Format

True and False are length-one logicals; None is NULL.

Value

These objects are exported constants, not functions. Referencing one yields its stored value: True is the length-one logical vector TRUE, False is the length-one logical vector FALSE, and None is NULL. They exist only so that seaborn code containing Python's True, False, and None literals evaluates in R unchanged.

Assign plot variables from data + role references

Description

Assign plot variables from data + role references

Usage

rb_assign_variables(data = NULL, ...)

Arguments

Value

A list with data (tidy frame, columns named by role), names (role -> original variable name), and types (role -> variable type).

Univariate Gaussian KDE matching scipy.stats.gaussian_kde

Description

Univariate Gaussian KDE matching scipy.stats.gaussian_kde

Usage

rb_gaussian_kde(
  x,
  bw_method = "scott",
  bw_adjust = 1,
  gridsize = 200,
  cut = 3,
  clip = NULL,
  weights = NULL,
  cumulative = FALSE
)

Arguments

Value

A list with x (grid) and y (density) vectors.

Histogram bin edges matching numpy.histogram_bin_edges

Description

Histogram bin edges matching numpy.histogram_bin_edges

Usage

rb_hist_bins(
  x,
  bins = "auto",
  binrange = NULL,
  binwidth = NULL,
  discrete = FALSE
)

Arguments

Value

A numeric vector of bin edges.

A continuous reaborn colormap

Description

Constructed internally by palette functions when as_cmap = TRUE. It wraps a 256-row RGB lookup table and is a function mapping values in ⁠[0, 1]⁠ to hex colors via matplotlib's floor-index quantization. The hex LUT is stored in attr(., "colors").

Usage

rb_make_cmap(lut256, name = "reaborn")

Matplotlib-style axis breaks

Description

Approximates matplotlib's MaxNLocator / AutoLocator (the default axis tick locator), which places ticks at "nice" round numbers using the step sequence 1, 2, 2.5, 5, 10. ggplot2's default (scales::extended_breaks) targets fewer ticks and lands on different values, so reaborn plots use this to match seaborn's gridline density and tick positions.

Usage

rb_mpl_breaks(n = 9)

Arguments

Value

A function of limits returning a numeric vector of break positions, suitable for the breaks argument of a ggplot2 continuous scale.

Wrap a ggplot as a reaborn plot

Description

Wrap a ggplot as a reaborn plot

Usage

reaborn_plot(plot, call = NULL)

Arguments

Value

The plot with class reaborn_plot prepended.

Plot data and a linear regression model fit

Description

Port of seaborn.regplot. The confidence band is a bootstrap interval, like seaborn. Returns a reaborn_plot.

Usage

regplot(
  data = NULL,
  x = NULL,
  y = NULL,
  order = 1,
  logistic = FALSE,
  lowess = FALSE,
  robust = FALSE,
  logx = FALSE,
  ci = 95,
  n_boot = 1000,
  seed = NULL,
  scatter = TRUE,
  fit_reg = TRUE,
  color = NULL,
  marker = "o",
  scatter_kws = NULL,
  line_kws = NULL,
  truncate = TRUE,
  x_jitter = NULL,
  y_jitter = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Figure-level interface for relational plots

Description

Port of seaborn.relplot. Draws scatterplot (kind = "scatter") or lineplot (kind = "line") onto a grid of facets defined by row/col. Returns a reaborn_plot (a faceted ggplot) with the legend outside, like a seaborn FacetGrid.

Usage

relplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  size = NULL,
  style = NULL,
  units = NULL,
  weights = NULL,
  row = NULL,
  col = NULL,
  col_wrap = NULL,
  row_order = NULL,
  col_order = NULL,
  palette = NULL,
  hue_order = NULL,
  hue_norm = NULL,
  sizes = NULL,
  size_order = NULL,
  size_norm = NULL,
  markers = NULL,
  dashes = NULL,
  style_order = NULL,
  legend = "auto",
  kind = "scatter",
  height = 5,
  aspect = 1,
  facet_kws = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Restore matplotlib/ggplot2 defaults

Description

Port of seaborn.reset_defaults / seaborn.reset_orig.

Usage

reset_defaults()

reset_orig()

Value

Invisibly NULL.

Plot the residuals of a linear regression

Description

Port of seaborn.residplot. Returns a reaborn_plot.

Usage

residplot(
  data = NULL,
  x = NULL,
  y = NULL,
  lowess = FALSE,
  order = 1,
  robust = FALSE,
  color = NULL,
  scatter_kws = NULL,
  line_kws = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Plot marginal rug ticks

Description

Port of seaborn.rugplot. Draws small ticks at each observation along the relevant axis. Returns a reaborn_plot (typically added to another plot, but usable standalone).

Usage

rugplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  height = 0.025,
  expand_margins = TRUE,
  palette = NULL,
  hue_order = NULL,
  hue_norm = NULL,
  legend = TRUE,
  ...
)

Arguments

Value

A reaborn_plot.

Increase the saturation of a color to its maximum

Description

Port of seaborn.saturate.

Usage

saturate(color)

Arguments

Value

A hex color string.

Draw a scatter plot with semantic mappings

Description

Port of seaborn.scatterplot. Returns a reaborn_plot (a ggplot), so it can be extended with any ggplot2 component.

Usage

scatterplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  size = NULL,
  style = NULL,
  palette = NULL,
  hue_order = NULL,
  hue_norm = NULL,
  sizes = NULL,
  size_order = NULL,
  size_norm = NULL,
  markers = TRUE,
  style_order = NULL,
  legend = "auto",
  ...
)

Arguments

Value

A reaborn_plot.

Change how single-letter color codes are interpreted

Description

Port of seaborn.set_color_codes. Returns (invisibly) the mapping from the single-letter codes ⁠b g r m y c k⁠ to the colors of the given seaborn palette, so reaborn helpers can resolve them like seaborn does.

Usage

set_color_codes(palette = "deep")

Arguments

Value

Invisibly, the named character vector of code -> hex mappings.

Independently set the hue, lightness, and/or saturation of a color

Description

Port of seaborn.set_hls_values.

Usage

set_hls_values(color, h = NULL, l = NULL, s = NULL)

Arguments

Value

A hex color string.

Set the matplotlib color cycle / ggplot default discrete palette

Description

Port of seaborn.set_palette.

Usage

set_palette(palette, n_colors = NULL, desat = NULL, color_codes = FALSE)

Arguments

Value

Invisibly NULL.

Set multiple theme parameters in one step

Description

Port of seaborn.set_theme (and its alias set). Sets the global look used by subsequent reaborn (and ggplot2) plots.

Usage

set_theme(
  context = "notebook",
  style = "darkgrid",
  palette = "deep",
  font = "sans",
  font_scale = 1,
  color_codes = TRUE,
  rc = NULL
)

set(...)

Arguments

Value

Invisibly, the applied ggplot2::theme.

seaborn-style sns. function aliases

Description

For copy-paste compatibility with Python, reaborn provides a sns.-prefixed alias for every public plotting, palette, and theming function (R allows dots in identifiers). So pasted seaborn code such as sns.scatterplot(data = df, x = "a", y = "b", hue = "g") runs verbatim. Each alias is identical to its unprefixed counterpart; see that function for arguments and details.

Value

Each sns.-prefixed object is the exact same function as its unprefixed counterpart (sns.scatterplot <- scatterplot, and so on), so calling an alias returns precisely what the counterpart returns. By category: the plotting functions (e.g. sns.scatterplot(), sns.histplot(), sns.heatmap(), sns.pairplot(), sns.FacetGrid(), sns.palplot()) return a reaborn_plot object (a ggplot2/patchwork object that draws when printed), except the easter-egg sns.dogplot(), which prints an affirmation and returns NULL invisibly; the palette constructors (e.g. sns.color_palette(), sns.husl_palette(), sns.cubehelix_palette()) return a character vector of hex colors, or a reaborn_cmap; the color helpers sns.desaturate(), sns.saturate(), and sns.set_hls_values() return a hex color string; sns.axes_style() and sns.plotting_context() return a named list of style/context parameters; sns.despine() and sns.move_legend() return a ggplot2 theme object to add to a plot; sns.load_dataset() returns a data frame and sns.get_dataset_names() a character vector; and the theming setters (sns.set_theme(), sns.set(), sns.set_style(), sns.set_context(), sns.set_palette(), sns.set_color_codes(), sns.reset_defaults(), sns.reset_orig()) are called for their side effect of changing global plot defaults and return their value invisibly. See each unprefixed function's own help page for the precise structure and meaning of its return value.

Examples

pen <- load_dataset("penguins")
p <- sns.scatterplot(data = pen, x = "bill_length_mm", y = "bill_depth_mm",
                     hue = "species")

Draw a categorical scatter with jitter

Description

Port of seaborn.stripplot. Returns a reaborn_plot.

Usage

stripplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  order = NULL,
  hue_order = NULL,
  jitter = TRUE,
  dodge = FALSE,
  orient = NULL,
  color = NULL,
  palette = NULL,
  size = 5,
  edgecolor = "gray",
  linewidth = 0,
  legend = "auto",
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Draw a categorical scatter with non-overlapping points

Description

Port of seaborn.swarmplot, using a beeswarm layout. Returns a reaborn_plot.

Usage

swarmplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  order = NULL,
  hue_order = NULL,
  dodge = FALSE,
  orient = NULL,
  color = NULL,
  palette = NULL,
  size = 5,
  edgecolor = NULL,
  linewidth = 0,
  legend = "auto",
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.

Build a ggplot2 theme replicating a seaborn style + context

Description

Build a ggplot2 theme replicating a seaborn style + context

Usage

theme_seaborn(
  style = "darkgrid",
  context = "notebook",
  font_scale = 1,
  font = "sans"
)

Arguments

Value

A complete ggplot2::theme object.

Draw a violin plot

Description

Port of seaborn.violinplot. The kernel density matches scipy.stats.gaussian_kde. Returns a reaborn_plot.

Usage

violinplot(
  data = NULL,
  x = NULL,
  y = NULL,
  hue = NULL,
  order = NULL,
  hue_order = NULL,
  orient = NULL,
  color = NULL,
  palette = NULL,
  saturation = 0.75,
  fill = TRUE,
  inner = "box",
  split = FALSE,
  width = 0.8,
  dodge = "auto",
  gap = 0,
  linewidth = NULL,
  linecolor = "auto",
  cut = 2,
  gridsize = 100,
  bw_method = "scott",
  bw_adjust = 1,
  density_norm = "area",
  common_norm = FALSE,
  legend = "auto",
  inner_kws = NULL,
  .facet_vars = NULL,
  ...
)

Arguments

Value

A reaborn_plot.